Empowering weather and climate forecast
| # | Year | Publication | Download |
|---|---|---|---|
| 1 | 2021 | Chantry, M. et al.: "Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting". Journal of Advances in Modeling Earth Systems, 13, e2021MS002477. https://doi.org/10.1029/2021MS002477 | Download PDF |
| 2 | 2021 | Sonnewald, et al.: "Bridging observations, theory and numerical simulation of the ocean using machine learning". Environ. Res. Lett. 16 073008 | Download PDF |
| 3 | 2021 | Hatfield, et al.: "Building Tangent-Linear and Adjoint Models for Data Assimilation With Neural Networks". Journal of Advances in Modeling Earth Systems, 13, e2021MS002521 | Download PDF |
| 4 | 2021 | Agarwal et al.: "A Comparison of Data-Driven Approaches to Build Low-Dimensional Ocean Models". Journal of Advances in Modeling Earth Systems, 13, e2021MS002537 | Download PDF |
| 5 | 2021 | Kloewer et al.: "Compressing atmospheric data into its real information content". Nat Comput Sci 1, 713–724 (2021). https://doi.org/10.1038/s43588-021-00156-2 | Download PDF |
| 6 | 2022 | Rausch, Ben-Nun et al.: "A Data-centric Optimization Framework for Machine Learning". Proceedings of the 36th ACM International Conference on Supercomputing | Download PDF Download PDF |
| 7 | 2022 | Gong B, Langguth M, Ji Y, Mozaffari A, Stadtler S, Mache K, Schultz MG.: "Temperature forecasting by deep learning methods". EGU Geoscientific Model Development | Download PDF |
| 8 | 2022 | Dueben et al.: "Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook". Artificial Intelligence for the Earth Systems, 1(3), e210002. Retrieved Sep 1, 2022 | Download PDF |
| 9 | 2022 | Ashkboos et al.: "ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast". arXiv preprint arXiv:2206.14786 | Download PDF |
| 10 | 2022 | David Meyer et al.: "Machine learning emulation of urban land surface processes". Journal of Advances in Modeling Earth Systems, 14, e2021MS002744 | Download PDF |
| 11 | 2022 | David Meyer et al.: "Machine Learning Emulation of 3D Cloud Radiative Effects". Journal of Advances in Modeling Earth Systems, 14, e2021MS002550 | Download PDF |
| 12 | 2022 | Patrick Laloyaux et al.: "Deep learning to estimate model biases in an operational NWP assimilation system". Journal of Advances in Modeling Earth Systems, 14, e2022MS003016 | Download PDF |
| 13 | 2022 | Lorenzo Pacchiardi et al.: "Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization". arXiv preprint arXiv:2112.08217 | Download PDF |
| 14 | 2021 | Nikoli Dryden et al.: "Clairvoyant Prefetching for Distributed Machine Learning I/O". Supercomputing | Download PDF |
| 15 | 2021 | Shigang Li and Torsten Hoefler: "Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines". Supercomputing | Download PDF |
| 16 | 2022 | Shigang Li and Torsten Hoefler: "Near-Optimal Sparse Allreduce for Distributed Deep Learning". PPoPP | Download PDF |
| 17 | 2021 | Chris Cummins et al.: "ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations". International Conference on Learning Representations (ICLR) | Download PDF |
| 18 | 2022 | Bryan Plummer et al.: "Neural Parameter Allocation Search". International Conference on Learning Representations (ICLR) | Download PDF |
| 19 | 2022 | Tal Ben-Nun et al. : "Productive Performance Engineering for Weather and Climate Modeling with Python". Supercomputing | Download PDF |
| 20 | 2022 | Saleh Ashkboos et al.: "A Dataset For Post-Processing Ensemble Weather Forecast". Proceedings of the Neural Information Processing (NeurIPS) Systems Track on Datasets and Benchmarks | Download PDF |
| 21 | 2023 | Karthick Panner Selvam, Mats Brorsson: "Performance Analysis and Benchmarking of a Temperature Downscaling Deep Learning Model". 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing | Download PDF |
| 22 | 2023 | Ji, Y., Gong, B., Langguth, M., Mozaffari, A., and Zhi, X.: "CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting". Geoscientific Model Development | Download PDF |